A Fast Optical Coherence Tomography Angiography Image Acquisition and Reconstruction Pipeline for Skin Application

Traditional high-quality OCTA images require multi-repeated scans (e.g., 4-8 repeats) in the same position, which causes patient uncomfortable. We propose a deep-learning-based pipeline that can extract high-quality OCTA images from only two-repeat OCT scans. The performance of the proposed Image Reconstruction U-Net (IRU-Net) outperforms state-of-the-art UNet vision transformer and UNet in OCTA image reconstruction from a two-repeat OCT signal. The results demonstrated a mean peak-signal-to-noise ratio increased from 15.7 to 24.2; the mean structural similarity index measure improved from 0.28 to 0.59; while OCT data acquisition time was reduced from 21 seconds to 3.5 seconds (reduced by 83%)